Incremental rule learning based on example nearness from numerical data streams

Mining data streams is a challenging task that requires online systems based on incremental learning approaches. This paper describes a classification system based on decision rules that may store up-to-date border examples to avoid unnecessary revisions when virtual drifts are present in data. Consistent rules classify new test examples by covering and inconsistent rules classify them by distance as the nearest neighbor algorithm. In addition, the system provides an implicit forgetting heuristic so that positive and negative examples are removed from a rule when they are not near one another.

[1]  KlinkenbergRalf.  Learning drifting concepts: Example selection vs. example weighting , 2004 .

[2]  Claude Sammut,et al.  Extracting Hidden Context , 1998, Machine Learning.

[3]  Marcus A. Maloof,et al.  Incremental rule learning with partial instance memory for changing concepts , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[4]  Geoff Hulten,et al.  Mining time-changing data streams , 2001, KDD '01.

[5]  Ryszard S. Michalski,et al.  Incremental learning with partial instance memory , 2002, Artif. Intell..

[6]  Jesús S. Aguilar-Ruiz,et al.  Discovering decision rules from numerical data streams , 2004, SAC '04.

[7]  Svetha Venkatesh,et al.  Using multiple windows to track concept drift , 2004, Intell. Data Anal..

[8]  Lukasz Golab,et al.  Issues in data stream management , 2003, SGMD.

[9]  Rakesh Agrawal,et al.  SPRINT: A Scalable Parallel Classifier for Data Mining , 1996, VLDB.

[10]  Marcos Salganicoff,et al.  Tolerating Concept and Sampling Shift in Lazy Learning Using Prediction Error Context Switching , 1997, Artificial Intelligence Review.

[11]  Huan Liu,et al.  Handling concept drifts in incremental learning with support vector machines , 1999, KDD '99.

[12]  William Nick Street,et al.  A streaming ensemble algorithm (SEA) for large-scale classification , 2001, KDD '01.

[13]  J. C. Schlimmer,et al.  Incremental learning from noisy data , 2004, Machine Learning.

[14]  Philip S. Yu,et al.  Mining concept-drifting data streams using ensemble classifiers , 2003, KDD '03.

[15]  João Gama,et al.  Forest trees for on-line data , 2004, SAC '04.

[16]  Marcus A. Maloof,et al.  Dynamic weighted majority: a new ensemble method for tracking concept drift , 2003, Third IEEE International Conference on Data Mining.

[17]  Ruoming Jin,et al.  Efficient decision tree construction on streaming data , 2003, KDD '03.

[18]  Ralf Klinkenberg,et al.  Learning drifting concepts: Example selection vs. example weighting , 2004, Intell. Data Anal..

[19]  Gerhard Widmer,et al.  Learning in the Presence of Concept Drift and Hidden Contexts , 1996, Machine Learning.

[20]  Manfred K. Warmuth,et al.  The Weighted Majority Algorithm , 1994, Inf. Comput..

[21]  JOHANNES GEHRKE,et al.  RainForest—A Framework for Fast Decision Tree Construction of Large Datasets , 1998, Data Mining and Knowledge Discovery.